2 citations · 2 across the 5 of their papers we have counts for
6 papers
When Quantization Preserves Accuracy but Not Evidence: Explanation-Aware Post-Training Quantization for Medical LLMs
Yeji Kim, Mi-Young Kim, Randy Goebel
Post-training quantization (PTQ) enables efficient deployment of large language models, and PTQ methods are usually optimized and evaluated with generic reconstruction, perplexity,…
Does Role Specialization Matter for Explanation Faithfulness in Mixture-of-Experts?
Yeji Kim, Housam Babiker, Mi-Young Kim +1
Mixture-of-Experts (MoE) architectures have recently been extended with role-based mechanisms for interpretability. This is typically done by assigning semantic roles to individual…
An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving
Maryam Sadat Hosseini Azad, Shahriar Baradaran Shokouhi, Amir Abbas Hamidi Imani +2
The application of computer vision is gradually increasing across various domains. They employ deep learning models with a black-box nature. Without the ability to explain the beha…
Feature-level Interaction Explanations in Multimodal Transformers
Yeji Kim, Housam Khalifa Bashier Babiker, Mi-Young Kim +1
Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision. Most existing multimodal explainable AI (MXAI) methods ext…
Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles
Shahin Atakishiyev, Mohammad Salameh, Randy Goebel
Autonomous vehicles often make complex decisions via machine learning-based predictive models applied to collected sensor data. While this combination of methods provides a foundat…
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving
Shahin Atakishiyev, Mohammad Salameh, Randy Goebel
The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles (AVs), largely due to advances in deep learning, th…